In these experiments, adapted from [1]}, an exploration policy is trained with intrinsic motivation, using a model-based RL algorithm, based upon Dreamer [2]}. The exploration policy is used to collect experience from the environment, which is in turn used to improve the agent model. Trajectories sampled from the model, including predictions of the environment's rewards, are used to train an exploitative policy that is used sporadically and only for zero-shot evaluation (except Dreamer baseline).
